In performing face recognition for a set of subjects, certain issues need to be resolved, such as: spatial variability, scale variability, contrast variability, pose variability, occlusions, and the like. In many applications, especially in mobile applications, an online learning scheme is also desirable so that a recognition system can incorporate a growing set of subjects, learn new faces on the fly and dynamically adjust the candidate subject set according to context information.
Occlusion especially represents a significant obstacle to a robust face recognition process. This is mainly due to the unpredictable error caused by the occlusion. The occlusion can affect any part of a face image, and can be arbitrarily large in magnitude. On the other hand, the error caused by the occlusion typically corrupts only a portion of the image. Because of that, the error can have the sparse representation, which might be useful for low complexity reconstruction process.